Survey data on poverty and broad policy pointers

Source: The post is based on the article “Survey data on poverty and broad policy pointers” published in The Hindu on 12^th August 2022.

Syllabus: GS 2 – Issues relating to poverty and hunger.

Relevance: About the concerns associated with the NFHS data.

News: Academics have questioned the quality of NFHS data on poverty statistics for various reasons.

About India’s Multidimensional Poverty Index (MPI)

NITI Aayog used the survey data of NFHS 4 to estimate the Multidimensional Poverty Index (MPI) and published the baseline report in 2021. The MPI is a product of the Head Count Ratio and Intensity of Poverty.

Rationale for the MPI: poverty is the outcome of simultaneous deprivations in multiple functions such as attainments in health, education, and standard of living.

Calculating method: NITI Aayog identified 12 indicators in these three sectors and calculated the weighted average of deprivations. The proportion of the population with a deprivation score greater than 0.33 to the total population is defined as the Poverty Ratio or Head Count Ratio.

Estimation of the Intensity of Poverty: is the weighted-average deprivation score of the multidimensionally poor. For instance, the Intensity of Poverty in Tamil Nadu declined from 39.97% to 38.78% during this period.

What are the concerns associated with the NITI Aayog’s poverty index?

Firstly, the overall population was deprived in most of the indicators individually, and they were higher than the population identified as multidimensionally poor.

Secondly, the strength of the MPI as an instrument for a data-driven public policy depends on the quality of survey data, namely the NFHS data.

What are the factors affecting the quality of NFHS data?

The National Sample Survey Organisation’s (NSSO) sample surveys have been debated among economists and statisticians, both in terms of sampling and non-sample errors, right from its initial days in the 1950s.

The NFHS data were collected in two time periods. One before the pandemic and the other post-lockdown period. The difference in time period interprets the statistics of the entire database. For instance, the deprivation in terms of nutrition and maternal health declined, and schooling and school attendance increased in the post-lockdown period.

The other issues with NFHS data are a) Arbitrariness in reporting the age of the dead, b) Differences in data quality between educated and uneducated respondents, c) Data quality based on differences in time taken to complete a survey of different household types, d) Market-based approach to decide the data collection process, etc.

All these have serious implications for health data such as fertility and death rates.

What should be done with the NFHS Data and to reduce poverty?

For improving the NFHS: a) India should improve the sample design and response quality, b) Analysing the data and finding the inferences from different databases on an issue would help improve data gathering systems. c) The government must continue to use survey data both to derive policy conclusions and also to help improve data quality.

For reducing poverty: a) The survey data gives only broad policy pointers whereas programmatic interventions should be curated with ground-level realities, b) People may be deprived severely in a few functions, but may not be multidimensionally poor. Hence, attacking poverty should not only be multidimensional but also universal.